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on Labor Markets - Supply, Demand, and Wages |
| By: | Cristina Gualdani; Elena Pastorino; Áureo de Paula; Sergio Salgado |
| Abstract: | We examine the empirical content of a large class of dynamic matching models of the labor market with ex-ante heterogeneous firms and workers, symmetric uncertainty and learning about workers’ productivity, and firms’ monopsony power. We allow workers’ human capital, acquired before and after entry into the labor market, to be general across firms to varying degrees. Such a framework nests and extends known models of worker turnover across firms, occupational choice, wage growth, wage differentials across occupations, firms, and industries, and wage dispersion across workers and over the life cycle. We establish intuitive conditions under which the model primitives are semiparametrically identified solely from data on workers’ wages and jobs, despite the dynamics of these models giving rise to complex patterns of selection based on endogenously time-varying observable and unobservable characteristics of workers and firms. By relying on this identification argument, we develop a constructive estimator of the model primitives, which builds on common methods for mixture and extremal quantile regression models and displays standard properties. Through the lens of this framework, we investigate how well typical empirical wage measures of matching assortativeness and firms’ wage-setting power detect the degrees of sorting and monopsony power in the labor market, respectively. We show that usual measures of sorting severely understate its importance because they ignore the option value of worker human capital and the information about worker productivity acquired through employment, in terms of higher future wages and improved future sorting, which is priced into current wages thus depressing them. We also demonstrate how the markdown of wages relative to output largely overstates firms’ labor market power by ignoring that this option value, which captures future returns from acquired human capital and information, generally lowers wages. We find evidence of both of these features in U.S. data by documenting a strong degree of labor market sorting once appropriately measured and, correspondingly, a lower degree of firm monopsony power than typically documented. |
| JEL: | E20 H0 J31 J42 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34973 |
| By: | István Boza (ELTE Centre for Economic and Regional Studies); Dániel Horn (Corvinus University Budapest; ELTE Centre for Economic and Regional Studies) |
| Abstract: | This paper introduces a framework that combines a traditional Mincer wage equation with an Abowd–Kramarz–Margolis (AKM) decomposition in a unified linear framework. The approach allows pre-labor market entry group-level factors to be mapped transparently onto the underlying channels of wage determination, including individual earning capacity, firm sorting, and occupational allocation. Applying the method to linked employer–employee administrative data from Hungary, we study how secondary schools are related to early-career wage inequality. Secondary school affiliation explains about 15% of wage variation among young workers, with a substantial share operating through sorting into firms and occupations. Controlling for completed educational attainment reduces school effects. However, these effects do not disappear completely and persist even after controlling for pre-existing differences in student pools measured around the age of 14-15. More broadly, the framework provides a general tool for studying how institutions shape labor-market outcomes through multiple economic channels. |
| Keywords: | wage inequality, school effects, AKM decomposition, Mincer equation, employer–employee linked data, labor market outcomes |
| JEL: | J31 J24 I26 C32 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:has:discpr:2604 |
| By: | Haotian Deng (Department of Economics, Ghent University, Belgium); Sam Desiere (Department of Economics, Ghent University, Belgium; IZA Institute of Labor Economics, Germany); Bart Cockx (Department of Economics, Ghent University, Belgium; IZA Institute of Labor Economics, Germany; IRES/LIDAM, UCLouvain, Belgium; CESIfo, Germany; ROA, Maastricht University, the Netherlands); Gert Bijnens (National Bank of Belgium) |
| Abstract: | This paper studies how employment subsidies for start-ups shape their performance. We exploit an unexpected policy reform in Belgium that permanently exempted start-ups hiring their first employee from payroll taxes for that employee. Using firm-level administrative data and a regression-discontinuity-in-time design, we find that subsidized post-reform startups employed fewer workers and generated lower output, value added, and profits compared to pre-reform start-ups. However, post-reform start-ups were more likely to survive as employers. These effects emerged within the first year after hiring and remained stable over a medium horizon of three years. Our findings indicate a compositional shift: the subsidy primarily induced low-productivity firms to enter the market. As most firms nowadays are nonemployers, our results meaningfully generalize the theoretical implications of standard neoclassical entrepreneurship models (employee–employer margin) and fill the important gap of the nonemployer–employer margin. |
| Keywords: | entrepreneurship; start-up; employment subsidy; tax reduction; labor demand; small firms |
| JEL: | H25 J23 J24 J38 L25 L26 M51 |
| Date: | 2026–02–04 |
| URL: | https://d.repec.org/n?u=RePEc:ctl:louvir:2026004 |
| By: | Michael Amior; Shmuel San |
| Abstract: | Firms face significant constraints in their ability to differentiate pay by worker productivity. We show how these internal equity constraints generate a quantity-quality trade-off in hiring: firms which offer higher wages attract higher skilled workers, but cannot profitably employ lower skilled workers. In equilibrium, this results in workplace segregation and pay dispersion even among ex-ante identical firms. Our framework provides a novel interpretation of the (empirically successful) log additive AKM wage model, and shows how log additivity can be reconciled with sorting of high-skilled workers to high-paying firms. It can also rationalize a hump-shaped relationship between firm size and firm pay, and provides new insights into aggregate-level, regional and sectoral variation in earnings inequality - which we explore using Israeli administrative data. |
| Keywords: | wages, productivity, labour, labor, skills |
| Date: | 2026–03–16 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2161 |
| By: | Salomé Baslandze; Zachary Edwards; John Graham; Ty McClure; Brent H. Meyer; Michael Sparks; Sonya R. Waddell; Daniel Weitz |
| Abstract: | We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation-and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI. |
| JEL: | D22 D24 G0 J01 J24 M15 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34984 |
| By: | Andreas de Barros; Theresa Lubozha |
| Abstract: | Whether targeted foundational instruction yields broad, long-term human capital gains is central to education policy but largely untested. We provide causal evidence from Zambia's government-run foundational skills program in public primary schools. After two years, a randomized trial shows the program increases literacy by 0.10 and numeracy by 0.15 standard deviations. In mathematics, effects on targeted skills are 2.6 times larger than on comprehensive assessments, without detectable transfer to adjacent domains. Adding professional development doubles per-pupil costs without additional learning gains. Despite limited short-run transfer, event-study estimates show positive effects on grade-7 language and mathematics exam scores in early adolescence. |
| Keywords: | field experiment, foundational skills, human capital, long-term effects, skill formation, skill transfer |
| JEL: | C93 H52 I21 I28 J24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12542 |
| By: | Mariagrazia Cavallo; Elizabeth Dhuey; Luca Fumarco; Levi Halewyck; Simon ter Meulen |
| Abstract: | This article reviews the growing literature on age at school entry and its effects over the life course. Age at school entry affects a broad range of outcomes, including education, labor-market performance, health, social relationships, and family formation. We synthesize the evidence using a conceptual framework that distinguishes four empirically intertwined components of age at school entry: starting age, age at outcome, relative age, and time in school. Within this framework, we highlight six key channels through which age at school entry operates. While the effects of age at school entry are often substantial and persistent, many studies estimate bundled impacts without isolating specific components or directly measuring underlying mechanisms. We explain how different research designs capture distinct combinations of these components. We also highlight how institutional heterogeneity and behavioral responses can complicate the interpretation of results. We conclude by outlining directions for future research and policy design. |
| Keywords: | age at school entry, Relative age, School starting age, Institutional heterogeneity, Behavioral responses |
| JEL: | I12 I21 I24 I31 J12 J13 J24 K42 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12545 |
| By: | Salomé Baslandze; Zach Edwards; John Graham; Ty McClure; Brent Meyer; Michael Sparks; Sonya Ravindranath Waddell; Daniel J. Weitz |
| Abstract: | We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI. |
| Keywords: | artificial intelligence; productivity; technological change; labor markets; occupations |
| JEL: | O33 D22 J24 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedawp:102936 |
| By: | Vittoria Dicandia |
| Abstract: | The wage gap between Black and white Americans narrowed in the 1960s-1970s but stagnated after 1980. This study argues that routine-biased technological change (RBTC) contributed to this stagnation by affecting Black and white male workers differently across the wage distribution. Using new empirical evidence on occupational patterns and wage determinants for these workers, I rationalize these patterns with a novel RBTC theoretical framework. Contrary to expectations, Black workers' employment in routine-intensive occupations increased, while white workers experienced a significant decline. Applying the Oaxaca-RIF decomposition, I show that occupational sorting amplifies wage gaps, particularly at the lower end of the wage distribution. These findings, interpreted through the novel theoretical framework, offer new insights into the mechanisms driving racial wage gaps at the close of the twentieth century. |
| Keywords: | technological change; wage differentials |
| JEL: | O33 J31 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:102932 |
| By: | David Autor; Caroline Chin; Anna M. Salomons; Bryan Seegmiller |
| Abstract: | We study the role of expertise in new work–novel occupational roles that emerge as technological and economic conditions evolve–using newly available 1940 and 1950 Census Complete Count files and confidential American Community Survey data from 2011-2023. We show that new work is systematically distinct from simply more work in existing occupations in four respects. First, it attracts workers with distinct characteristics: new work is disproportionately performed by younger and more educated workers, even within detailed occupation-industry cells. Second, new work commands economically significant wage premiums that persist beyond workers' initial entry into new work, consistent with returns to scarce, specialized expertise rather than temporary market disequilibrium. Third, these premiums decline across vintages as expertise diffuses, with 'newer' new work commanding larger premiums than older new work. Fourth, the emergence of new work can be traced to specific demand shocks in particular locations and time periods, suggesting that expertise formation responds systematically to economic opportunities. These findings suggest that new work serves as a countervailing force to automation-driven job displacement not merely by creating additional employment, but also by generating new domains of human expertise that command market premiums. This expertise-based mechanism helps explain both the expanding variety of work activities across decades and the historical resilience of the labor share. |
| JEL: | E24 J11 J23 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34986 |
| By: | Mallory Avery (Department of Economics, Monash University); Edwin Ip (Department of Economics, University of Exeter); Andreas Leibbrandt (Department of Economics, Monash University); Joseph Vecci (Department of Economics, University of Gothenburg) |
| Abstract: | Recent technological advancements are reshaping pathways to employment by automating the interview process. Asynchronous interviews, in which job applicants submit answers to interview questions via an online platform without interacting with an interviewer, are replacing more traditional face-to-face job interviews. At the same time, AI algorithms are now widely used to assess these interview answers. In this paper, we use a field experiment to comprehensively study how these new technologies affect applicants and employers in the recruitment process. Over 3, 000 job applicants are randomized into asynchronous audio or video interviews, live online interviews, and a control group. Their job interviews are then assessed by both professional recruiters and a commercial AI recruitment tool used by most Fortune 100 companies. We find that asynchronous interviews cause an over 50% decrease in application continuation, including among the most qualified applicants, and that this decline is largest for women. A complementary vignette experiment provides evidence that this deterrence is driven by perceptions about the competitiveness and fairness of the recruitment process. In terms of assessments, we find that the AI evaluation tool scores women and underrepresented racial minorities higher than human evaluators, while the opposite is true for men, Whites and Asians. We track our applicants' subsequent labor market outcomes and find that the AI assessment tool predicts subsequent employment success substantially better than human recruiters, suggesting that AI captures soft skills and potential that humans overlook. In addition, we provide evidence that, unlike AI, human recruiters' assessments suffer from multiple cognitive biases. Our findings provide some of the first key evidence on how recent technological advances are transforming the hiring process. |
| Keywords: | technological change, artificial intelligence, gender, field experiment |
| JEL: | C93 J23 J71 J78 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:exe:wpaper:2602 |
| By: | Annie Liu; Pinghui Wu |
| Abstract: | Educational attainment profoundly shapes cumulative earnings trends across US birth cohorts. Between the 1933 and 1977 cohorts, men with an advanced degree experienced rising earnings in both the early-career (ages 25 to 44) and late-career (ages 45 to 64) stages, while those with a sub-baccalaureate education―and college graduates outside the 1951–1965 cohorts―saw minimal earnings growth. Women experienced broad-based gains, with larger increases among those with a bachelor’s or advanced degree. For less educated men, extended work life represented the primary growth margin in the late-career stage. While gaps between education groups widened, within-group dispersion rose across cohorts, particularly among men born between 1933 and 1957. These cohort-to-cohort changes emerged at labor market entry and persisted throughout the career cycle, indicating that the conditions in which careers begin critically shape long-run inequality dynamics. |
| Keywords: | educational attainment; long-term cumulative earnings; earnings disparities |
| JEL: | I24 I26 J24 J31 |
| Date: | 2026–03–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:102908 |
| By: | Niklas Engbom; Aniket Baksy; Daniele Caratelli |
| Abstract: | We quantify how structural changes in the U.S. labor market have contributed to wage stagnation over the past four decades by weakening the job ladder. Using Current Population Survey microdata from 1982–2023 and a partial-equilibrium job-ladder model, we estimate that employed workers today are about half as likely to receive a better-paying outside offer as they were in the 1980s. This decline is unlikely to reflect less efficient matching, weaker labor demand, or changes in workers' acceptance behavior. Instead, cross-state variation is consistent with rising employer concentration and the growing use of noncompete agreements having curtailed opportunities for job shopping. In a general equilibrium version of the model, we find that these changes have reduced annual real wage growth by 0.68 percentage points—roughly one-third of the post-1980 slowdown—with about two-thirds of the effect operating through equilibrium wage setting rather than mechanical reallocation. |
| JEL: | E2 J31 J62 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34981 |
| By: | Michael Graber; Morten Håvarstein; Magne Mogstad; Gaute Torsvik; Ola L. Vestad |
| Abstract: | The elasticity of taxable income (ETI) parameter is a key quantity in empirical analysis of tax policy and labor supply. We examine when a commonly applied class of ETI estimands can be used to learn about individuals’ ETI parameters and their (un)compensated elasticities of labor supply. We begin by providing necessary and sufficient conditions for these estimands to be given a causal interpretation as a positively weighted average of heterogeneous ETI parameters. We then apply these results to empirically analyze a reform of the Norwegian tax system that reduced the marginal tax rates on middle and high incomes. The estimated ETI parameters increase steadily with income, meaning high-income individuals are more responsive to tax changes than middle-income individuals. Next, we show how (un)compensated elasticities of labor supply can be bounded directly from the ETI estimands, or point identified by combining these estimands with estimates of earnings responses to lottery winnings. The results suggest an (un)compensated elasticity of 0.1 (0.0) for middle-income individuals. The (un)compensated elasticity estimates increase steadily with income to around 0.45 (0.3) for high-income individuals. These findings imply a substantial excess burden of taxation, and that reducing top-income tax rates would increase tax revenue. Our findings are also informative about how the intertemporal elasticity of substitution and the Frisch elasticity vary across the income distribution. |
| JEL: | C26 C36 H20 J22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34987 |
| By: | Luca Di Corato; Federica Esposito; Natalia Montinari |
| Abstract: | Gender quotas remain controversial despite evidence of their effectiveness in reducing labor market gender inequality. We study how informational narratives about quotas affect support, and how effects depend on pre-existing causal beliefs about inequality. In a pre-registered survey experiment with 2, 404 Italian workers and managers, we compare demand-side (discrimination, bias) versus supply-side (participation, confidence, role models) framings. All information increases unincentivized stated support, most strongly under demand-side narratives, but none affects the extensive margin of an incentivized donation, revealing a clear say-do gap. Conditional on donating, however, supply-side framing significantly raises amounts given. Open-ended responses show narratives reshape reasoning primarily among those with diffuse priors (generic cultural explanations). We formalize this in a simple model featuring misalignment costs and tail-driven effects: narrative success depends on the distribution of prior beliefs, which acts as a state variable determining optimal framing across contexts. |
| JEL: | D63 D83 J16 J22 J31 J71 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:bol:bodewp:wp1220 |
| By: | Daniel Parshall; Andrea Lopez-Luzuriaga |
| Abstract: | Existing frameworks for measuring AI's labor market exposure decompose imperfectly across distinct dimensions: whether AI can perform a task, whether deployment is physically feasible, and whether institutions permit it. We propose CDR, a three-axis ordinal taxonomy that separates these dimensions into Cognitive complexity (C0–C4), Deployment difficulty (D0–D4), and Regulatory restrictions (R0–R4), extending Autor's (2003) routine/non-routine x cognitive/manual framework into a finer-grained classification space suitable for measuring AI exposure. Applying CDR to the full O*NET task universe (23, 850 task-activity pairs across 923 occupations, classified via multi-model LLM consensus: Claude Sonnet 4.6, GPT-5-mini, Gemini 3 Flash, validated against flagship models), we find that 40.2% of U.S. economy-weighted labor time falls in tasks that are within current AI cognitive reach (C |
| Keywords: | AI; Task Exposure; Labor Time Allocation. |
| JEL: | O33 C49 J21 J22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-005 |
| By: | Marcos Lacasa-Cazcarra |
| Abstract: | We study the employment effects of the 22% increase in the Spanish minimum wage in 2019, focusing on young workers. Using census-grade administrative tax data covering the universe of formal wage bills and employment (Models 190/390 linked to personal income tax records), we construct several measures of treatment intensity, including two structurally grounded bite indicators based on the incidence of young minimum-wage workers and the implied increase in the wage bill obtained via Exponential Tilting. Difference-in-differences estimates with two-way fixed effects, dynamic event-study specifications, and robust confidence intervals from the HonestDiD framework all point to the same conclusion: the reform did not generate net disemployment effects for young workers. Point estimates of the elasticity are small and often positive, and confidence internals comfortably include zero even with sizable deviations from parallel trends. A triple-difference design exploiting pre-existing tourism dependence further shows that the sharp employment collapse of 2020 is primarily explained by the COVID-19 shock operating through tourism-intensive sectors, rather than by the minimum-wage hike itself. Our results suggest that, in the macroeconomic and institutional environment prevailing in Spain in 2019, with the minimum wage rising to around 60% of the average wage in a recovering economy, the labour market absorbed a large discrete increase in the wage floor without destroying aggregate youth employment. More broadly, the paper highlights how the choice of treatment definition, the use of census-grade data, robust DiD inference, and explicit modelling of concurrent shocks can shape conclusions about the effects of minimum-wage policies. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20809 |
| By: | Jeffrey Clemens; Olivia Edwards; Jonathan Meer; Joshua D. Nguyen |
| Abstract: | We analyze the effect of California's $20 fast food minimum wage (Assembly Bill 1228), enacted in September 2023 and implemented in April 2024, on consumer prices using the Bureau of Labor Statistics' Consumer Price Indices for food away from home across 21 metropolitan statistical areas. Food away from home prices in California's four in-sample MSAs increased by 3.3 to 3.6 percent relative to 17 control MSAs through December 2024. Our estimates are stable across a number of specifications. Placebo tests on price indices for goods and services that were not affected by the policy, including food at home, show no differential increases in California's MSAs. The price increases we estimate likely arise in part from spillovers to the full-service sector, as well as changes in the production functions and product quality choices of limited service restaurants. |
| JEL: | J30 J38 L52 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34990 |
| By: | Simon Cordes; Max Müller |
| Abstract: | Labor supply depends on wages and amenities, and standard models implicitly assume that firms hold accurate beliefs about workers’ amenity valuations. In a survey with firms and workers in Germany, we measure workers’ valuations of amenities and firms’ beliefs about workers’ valuations. We find that firms systematically underestimate workers’ valuations of all amenities. These misperceptions are driven by interpersonal projection: managers project their own preferences—they value amenities less—onto workers. Through the lens of a simple model of imperfect competition, we show that firm misperceptions result in (i) labor shortages and (ii) excess labor costs for biased firms, and increase the market power of unbiased firms. Empirical tests confirm these predictions: a simple calibration suggests that non-providing firms could reduce their labor costs by 5% by providing amenities. |
| Keywords: | Amenities, Behavioral Firms, Labor Shortages, Work from Home, Beliefs |
| JEL: | J32 J42 J81 D2 D83 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_739 |
| By: | Drydakis, Nick |
| Abstract: | Artificial intelligence (AI) is increasingly recognised as a key driver of business innovation, yet its adoption among small and medium-sized enterprises (SMEs) varies considerably. This study examines whether AI Capital, defined as AI-related knowledge, skills and capabilities, is associated with business innovation among SMEs in England. Using a two-wave longitudinal panel dataset comprising 504 observations from SMEs collected in 2024 and 2025, the study develops and validates a 45-item AI Capital of Business scale. Business innovation is measured across five dimensions: product and service innovation, process innovation, technology adoption, market and customer engagement, and organisational culture and strategy. Regression models, including pooled OLS, Random Effects, and Fixed Effects specifications, are employed. The findings reveal a robust positive association between AI Capital and business innovation across all model specifications. This association holds across all business innovation dimensions and remains consistent for SMEs with differing levels of financial performance, size, and operational maturity. Each component of AI Capital independently exhibits a positive association with business innovation outcomes. The results highlight the central role of AI Capital in enabling SMEs to translate AI adoption into tangible business innovation. From a policy perspective, the findings indicate the value of targeted interventions that prioritise AI upskilling, organisational capability development, and accessible support mechanisms to promote inclusive and sustainable AI-driven business innovation among SMEs. |
| Keywords: | Artificial Intelligence, Artificial Intelligence Capital, Business Innovation, Innovation, SMEs |
| JEL: | O31 O33 O32 L26 L25 M15 D83 J24 O14 O39 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1723 |
| By: | Lorenzo Cappellari (Catholic University of Milan, Italy); Antonio Di Paolo (AQR-IREA, Universitat de Barcelona, Spain.); Thompson Ogajah Tawiah (AQR-IREA, Universitat de Barcelona, Spain.) |
| Abstract: | In this paper, we investigate the relationship between language background and labour market outcomes in the bilingual labour market of the Spanish region of Catalonia. The empirical analysis draws on repeated cross-sectional data that allow us to construct a quantitative measure of linguistic distance based on respondents’ native language, computed with respect to Catalan, the local language of Catalonia. As labour market outcomes, we consider employment probability and occupational quality, proxied by an indicator for holding a high-skilled job and by an ordinal measure of occupational skill level. The results indicate that, conditional on place of origin and a set of predetermined individual characteristics and controlling for origin-specific trends in years since migration, linguistic distance is not associated with employment. However, it is negatively related to both the likelihood of holding a high-skilled job and occupational skill levels. We analyse the role of language skills as a mechanism, showing that oral and written proficiency in Catalan are key drivers of the negative relationship between linguistic distance and occupational quality. Moreover, this relationship does not appear to be confounded by proficiency in Spanish, and the overall results are robust to a battery of robustness checks. Finally, the analysis of heterogeneous effects reveals an employment penalty associated with linguistic distance among females, and shows that its association with occupational quality is entirely driven by highly educated workers. |
| Keywords: | Linguistic Distance, Employment, Occupation, Multilingualism. JEL classification: J15; J24; J61; Z13. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202608 |
| By: | Michael Blank; Gregor Schubert; Miao Ben Zhang |
| Abstract: | This paper studies the impact of generative AI on U.S. households' task allocation at home, using detailed Internet browsing data from a large sample of home devices between 2021 and 2024. Leveraging pre-ChatGPT browsing patterns, we measure households' exposure to ChatGPT and use it as an instrument for ChatGPT adoption during the post-release period. Our IV estimates show that adopting generative AI substantially increases leisure browsing on home devices while leaving time spent on productive digital tasks unchanged. To examine mechanisms, we infer the purpose of households' ChatGPT use from surrounding internet activity and find that households primarily employ it for productive non-market tasks. Together, these results suggest that generative AI frees up leisure time by raising the efficiency of productive digital activities. Interpreting these findings through a standard time-allocation model implies economically large productivity gains from generative AI at home. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.03144 |
| By: | Chirowodza, Joe |
| Abstract: | This paper examines gendered mobility patterns for early career workers, focusing on family motivated job changes. Using the Swiss Household Panel data (1999-2023) we use multinomial logit, fixed effects, and event study models to understand the impact of family related job mobility on early career workers. The paper shows that compared to men; women are more likely to cite family reasons for job change. Women who change jobs for family reasons face wage stagnation although they earn improvements in specific satisfaction dimensions whilst overall job satisfaction is lower as compared to career motivated job mobility. We also find that job mobility rates for mothers remain constant around childbirth and among mothers who change jobs, family considerations emerge reactively post birth. The results have policy implications for early career job mobility which include subsiding childcare, standardizing flexibility at work and increasing paternity leave periods. |
| Keywords: | Job mobility, Early career, family motivated mobility, gendered mobility patterns, job satisfaction |
| JEL: | J13 J24 J62 |
| Date: | 2026–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127809 |
| By: | Jacob Bastian |
| Abstract: | While the correlation between working and voting is positive, I provide the first causal evidence that this relationship is negative. Using five decades of Earned Income Tax Credit (EITC) expansions and 1990s welfare reform as instruments for employment, I find that working lowers voter turnout and increases conservatism among lower-income mothers. Voter registration, political knowledge, and civic engagement decline, while preferences for conservative policies rise. Effects are largest for unmarried, younger, and less-educated mothers and are substantially stronger outside metropolitan areas. Notably, political shifts are concentrated among White women despite larger employment gains among non-White women, driven in part by White women entering more conservative coworker environments. Prior exposure to work also matters: women without working mothers experience larger ideological shifts. While recent decades have seen more women voting Democrat, even more women would have voted Democrat if not for decades of pro-work public policy targeting lower-income mothers. |
| JEL: | D72 H24 J22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34980 |
| By: | Nozaki, Yuko |
| Abstract: | Using data from the 2010 birth cohort of the Longitudinal Survey of Newborns in the 21st Century, this paper examines how parental nonstandard work schedules (NSWS) and children's screen time are associated with children's weekday sleep duration, with particular attention to sex heterogeneity. The analysis reveals a modest but statistically significant reduction in daughters’ weekday sleep (approximately 4 minutes per night), whereas no corresponding association is detected for sons. Paternal NSWS is not significantly related to sleep duration for either sex, a pattern that is consistent with limited variation and measurement constraints in fathers’ work schedules in this dataset. Longer screen time (television viewing and video gaming) is strongly associated with shorter sleep for both boys and girls. Overall, the results suggest that the direct impact of parental NSWS on children’s sleep is limited in magnitude, but that attention to children’s screen use and family-friendly scheduling for mothers working nonstandard hours—especially in families with daughters—are likely to support healthier sleep habits. |
| Keywords: | Nonstandard work schedules; Child sleep; Screen time; Sex differences. |
| JEL: | I12 J22 |
| Date: | 2026–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127834 |
| By: | Clara Schäper; Katharina Wrohlich; Sabine Zinn |
| Abstract: | Many countries use job-retention schemes, such as short-time work (STW), to stabilize the labor market during economic downturns. While these schemes might prevent unemployment (UE) and its adverse effects on workers, STW could also deter workers from moving to more productive firms, thereby negatively affecting their labor market outcomes in the long run. We analyze the long-term effects of STW and UE on individual workers using survey data from the SOEP for 1984–2023, which allows us to examine a broad set of yearly measured outcome variables, including employment, weekly working hours, real hourly wages, time spent on unpaid care work and life satisfaction. For the empirical analysis, we employ a two-step procedure that includes propensity score matching and an event-study model with individual fixed effects. Our findings suggest that, in the German institutional context, STW had no significant negative effects on workers’ labor market outcomes in the financial crisis of 2008/2009 and the economic crisis caused by the COVID-19 pandemic. This suggests that STW did not deter workers from switching to more productive firms. For the economic crisis following German reunification in the 1990s, however, we find that STW negatively affects workers’ long-term outcomes, albeit less strongly than episodes of UE. These findings suggest that the stabilizing effect of STW strongly depends on the economic context. |
| Keywords: | labor market shocks, job loss, short-time work, unemployment, event-study analysis |
| JEL: | H31 E32 J13 J16 J22 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2160 |